Large-scale monitoring of shorebird populations using count data andN-mixture models: Black Oystercatcher (Haematopus bachmani) surveys by land and sea

The Auk ◽  
2012 ◽  
Vol 129 (4) ◽  
pp. 645-652 ◽  
Author(s):  
James E. Lyons ◽  
J. Andrew Royle ◽  
Susan M. Thomas ◽  
Elise Elliott-Smith ◽  
Joseph R. Evenson ◽  
...  
Keyword(s):  
Mixture Models ◽  
Count Data ◽  
Large Scale

scholarly journals Small-variance asymptotics for non-parametric online robot learning

2018 ◽  
Vol 38 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Ajay Kumar Tanwani ◽  
Sylvain Calinon
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Large Scale ◽  
Principal Component ◽  
Small Variance ◽  
Remote Manipulation ◽  
State Duration ◽  
Duration Information ◽  
Low Dimensional ◽  
Non Parametric

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on a Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on a hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviors by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.

scholarly journals Disease‐structured N ‐mixture models: A practical guide to model disease dynamics using count data

2019 ◽  
Vol 9 (2) ◽  
pp. 899-909 ◽  
Author(s):  
Graziella V. DiRenzo ◽  
Christian Che‐Castaldo ◽  
Sarah P. Saunders ◽  
Evan H. Campbell Grant ◽  
Elise F. Zipkin
Keyword(s):  
Mixture Models ◽  
Count Data ◽  
Disease Dynamics ◽  
Practical Guide

Mixture Models for the Analysis of Repeated Count Data

1995 ◽  
Vol 44 (4) ◽  
pp. 473 ◽  
Author(s):  
Marijtje A. J. van Duijn ◽  
Ulf Bockenholt
Keyword(s):  
Mixture Models ◽  
Count Data

scholarly journals Augmenting machine learning photometric redshifts with Gaussian mixture models

2020 ◽  
Vol 498 (4) ◽  
pp. 5498-5510
Author(s):  
P W Hatfield ◽  
I A Almosallam ◽  
M J Jarvis ◽  
N Adams ◽  
R A A Bowler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mixture Models ◽  
Test Data ◽  
Galaxy Formation ◽  
Large Scale ◽  
Near Infrared ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Magnitude Distribution ◽  
Deep Fields

ABSTRACT Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies – almost all of which must be derived from photometry rather than spectroscopy. In this paper, we investigate how using statistical models to understand the populations that make up the colour–magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift estimates. In particular, we combine the use of Gaussian mixture models with the high-performing machine-learning photo-z algorithm GPz and show that modelling and accounting for the different colour–magnitude distributions of training and test data separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the algorithm. These methods are illustrated using data from deep optical and near-infrared data in two separate deep fields, where training and test data of different colour–magnitude distributions are constructed from the galaxies with known spectroscopic redshifts, derived from several heterogeneous surveys.

scholarly journals Marginalized mixture models for count data from multiple source populations

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Habtamu K. Benecha ◽  
Brian Neelon ◽  
Kimon Divaris ◽  
John S. Preisser
Keyword(s):  
Mixture Models ◽  
Count Data ◽  
Multiple Source ◽  
Source Populations
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